Cat and Dog Sketch Classifier

This is a machine learning model trained to differentiate between sketches of cats and dogs. It was built as part of a learning project to understand how AI models work and how to train them.

Model Details

Model Type: Convolutional Neural Network (CNN)
Training Data: Quick, Draw! dataset (cat and dog sketches)
License: MIT License
Supported Tasks: Image Classification

Usage

To use this model, you can follow these steps:

Load the Model:

import torch
from model import SimpleCNN
model = SimpleCNN()
model.load_state_dict(torch.load('cat_dog_classifier.bin'))
model.eval()

Predict an Image:

from PIL import Image
import numpy as np
import torch

def predict_image(model, image):
    # Preprocess the image
    if isinstance(image, Image.Image):
        image = image.resize((28, 28)).convert('L')
        image = np.array(image).astype('float32') / 255.0
    elif isinstance(image, np.ndarray):
        if image.shape != (28, 28):
            image = Image.fromarray(image).resize((28, 28)).convert('L')
            image = np.array(image).astype('float32') / 255.0
    else:
        raise ValueError("Image must be a PIL Image or NumPy array.")

    image = image.reshape(1, 1, 28, 28)
    image_tensor = torch.tensor(image).to(device)

    # Get prediction
    model.eval()
    with torch.no_grad():
        output = model(image_tensor)
        _, predicted = torch.max(output.data, 1)
    return 'cat' if predicted.item() == 0 else 'dog'

# Example usage
image = Image.open('path/to/your/image.png')
prediction = predict_image(model, image)
print(prediction)

Training the Model

To train the model yourself, use the provided train_cat_dog_classifier.py script.

License

This project is licensed under the MIT License. See the LICENSE file for more details.